A Multi-objective Approach to Solve the Location Areas Problem

  • Víctor Berrocal-Plaza
  • Miguel A. Vega-Rodríguez
  • Juan M. Sánchez-Pérez
  • Juan A. Gómez-Pulido
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7505)


The Public Land Mobile Networks (PLMN) are designed to provide anywhere, any kind, and anytime services to either static or moving users, therefore mobile location management is a fundamental tool in these systems. One of the techniques used in mobile location management is the location areas strategy, which set out the problem as an optimization problem with two costs, location update and paging. In this paper we resort to a multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), for finding quasi-optimal solutions of this optimization problem. At present, there is not any previous work that addresses the problem in a multi-objective manner, so we compare our results with those obtained by mono-objective algorithms from other authors. Results show that, for this problem, better solutions are achieved when each objective is treated separately.


Mobile Location Management Multi-Objective Optimization Non- Dominated Sorting Genetic Algorithm Location Areas Problem 


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  1. 1.
    Taheri, J., Zomaya, A.: A genetic algorithm for finding optimal location area configurations for mobility management. In: Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary, pp. 568–577. IEEE Computer Society, Washington (2005)Google Scholar
  2. 2.
    Almeida-Luz, S.M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Differential evolution for solving the mobile location management. Applied Soft Computing 11(1), 410–427 (2011)CrossRefGoogle Scholar
  3. 3.
    Demestichas, P., Georgantas, N., Tzifa, E., Demesticha, V., Striki, M., Kilanioti, M., Theologou, M.: Computationally efficient algorithms for location area planning in future cellular systems. Computer Communications 23(13), 1263–1280 (2000)CrossRefGoogle Scholar
  4. 4.
    Taheri, J., Zomaya, A.: A Simulated Annealing approach for mobile location management. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, pp. 194–201. IEEE Computer Society, Washington (2005)CrossRefGoogle Scholar
  5. 5.
    Gondim, P.: Genetic algorithms and the location area partitioning problem in cellular networks. In: Proceedings of IEEE 46th Vehicular Technology Conference, ‘Mobile Technology for the Human Race’, vol. 3, pp. 1835–1838 (1996)Google Scholar
  6. 6.
    Taheri, J., Zomaya, A.: A combined genetic-neural algorithm for mobility management. Journal of Mathematical Modeling and Algorithms 6(3), 481–507 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Taheri, J., Zomaya, A.: The use of a Hopfield neural network in solving the mobility management problem. In: Proceedings of The IEEE/ACS International Conference on Pervasive Services, pp. 141–150. IEEE Computer Society, Washington (2004)CrossRefGoogle Scholar
  8. 8.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  9. 9.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Víctor Berrocal-Plaza
    • 1
  • Miguel A. Vega-Rodríguez
    • 1
  • Juan M. Sánchez-Pérez
    • 1
  • Juan A. Gómez-Pulido
    • 1
  1. 1.Dept. Technologies of Computers & CommunicationsUniversity of Extremadura Escuela PolitécnicaCáceresSpain

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